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Neuroimage. 2017 Apr 1;149:424-435. doi: 10.1016/j.neuroimage.2016.12.037. Epub 2017 Feb 20.

Heterogeneous fractionation profiles of meta-analytic coactivation networks.

Author information

1
Department of Physics, Florida International University, Miami, FL, USA. Electronic address: alaird@fiu.edu.
2
Department of Physics, Florida International University, Miami, FL, USA.
3
School of Computing and Information Sciences, Florida International University, Miami, FL, USA.
4
Research Imaging Center, University of California Davis, Sacramento, CA, USA.
5
Institute of Clinical Neuroscience and Medical Psychology, Heinrich-Heine University, Düsseldorf, Germany; Institute of Neuroscience and Medicine, Research Center Jülich, Jülich, Germany.
6
Oxford Centre for Functional MRI of the Brain, University of Oxford, Oxford, UK.
7
Research Imaging Institute, University of Texas Health Science Center, San Antonio, TX, USA; Research Service, South Texas Veterans Administration Medical Center, San Antonio, TX, USA; State Key Laboratory for Brain and Cognitive Sciences, University of Hong Kong, Hong Kong.
8
Department of Psychology, Florida International University, Miami, FL, USA.

Abstract

Computational cognitive neuroimaging approaches can be leveraged to characterize the hierarchical organization of distributed, functionally specialized networks in the human brain. To this end, we performed large-scale mining across the BrainMap database of coordinate-based activation locations from over 10,000 task-based experiments. Meta-analytic coactivation networks were identified by jointly applying independent component analysis (ICA) and meta-analytic connectivity modeling (MACM) across a wide range of model orders (i.e., d=20-300). We then iteratively computed pairwise correlation coefficients for consecutive model orders to compare spatial network topologies, ultimately yielding fractionation profiles delineating how "parent" functional brain systems decompose into constituent "child" sub-networks. Fractionation profiles differed dramatically across canonical networks: some exhibited complex and extensive fractionation into a large number of sub-networks across the full range of model orders, whereas others exhibited little to no decomposition as model order increased. Hierarchical clustering was applied to evaluate this heterogeneity, yielding three distinct groups of network fractionation profiles: high, moderate, and low fractionation. BrainMap-based functional decoding of resultant coactivation networks revealed a multi-domain association regardless of fractionation complexity. Rather than emphasize a cognitive-motor-perceptual gradient, these outcomes suggest the importance of inter-lobar connectivity in functional brain organization. We conclude that high fractionation networks are complex and comprised of many constituent sub-networks reflecting long-range, inter-lobar connectivity, particularly in fronto-parietal regions. In contrast, low fractionation networks may reflect persistent and stable networks that are more internally coherent and exhibit reduced inter-lobar communication.

KEYWORDS:

BrainMap; Fractionation; Independent component analysis; Meta-analytic coactivation networks; Meta-analytic connectivity modeling; Neuroimaging meta-analysis; Neuroinformatics

PMID:
28222386
PMCID:
PMC5408583
DOI:
10.1016/j.neuroimage.2016.12.037
[Indexed for MEDLINE]
Free PMC Article

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